System, device and method for imaging of objects using signal clustering

ABSTRACT

Methods and a device for imaging objects including unsupervised classifying and data analyzing of the object to detect and identify the structure of the object and further display the object&#39;s structure underlying structure, for example the arrangement of and relationships between the parts or elements of the object by using a location module configured to record the physical location of an antenna array.

CROSS-REFERENCE

The present application claims the benefit of U.S. ProvisionalApplication Ser. No. 62/260,582, filed on Nov. 29, 2015, entitled“SYSTEM, DEVICE AND METHOD FOR IMAGING OF OBJECTS USING SIGNALCLUSTERING” (attorney docket no. VY024/USP) which is incorporated hereinby reference in its entirety.

The subject matter of the present application is also related to PCTApplication PCT/IL2016/050440, filed on Apr. 26, 2016, entitled “SYSTEMDEVISE AND METHOD FOR ESTIMATING DIELECTRIC MEDIA PARAMETERS” (attorneydocket no. VY015/PCT); PCT Application PCT/IL2016/050448, filed on Apr.29, 2016, entitled “SYSTEM, DEVICE AND METHOD FOR LOCALIZATION ANDORIENTATION OF ANTENNA ARRAY FOR RADIO FREQUENCY IMAGING” (attorneydocket VY016/PCT); each of which is incorporated herein by reference inits entirety.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to a system, device and method for imagingan object or substances and more specifically, but not exclusively, toMIMO (multiple-input and multiple-output) radar imaging such as in-wallimaging in the UWB (ultra-wideband) frequency range.

BACKGROUND

A known and unsolved problem, which challenges rescue forces such as thepolice and army on their day-to-day activity, includes the detection ofhidden objects, such as weapons or explosives hidden behind a wall of abuilding, concealed on individuals, or underground. In-wall (or throughwall) imaging solution may also be desirable for use as part as a handheld device such as a mobile device, e.g. smart phone tablet or PC for avariety of applications to provide the mobile devise user informationrelated to data in areas that cannot be imaged via the devise's standardcamera.

Known solutions for in-wall imaging include for example radio frequency(RF) and other sensing methods to penetrate wall materials and optimallyestimate the content and structure of an object such as wall.

Other known solutions which include imaging objects and substances suchas in-wall imaging, for example imaging walls made of hollow-blocks,drywalls, or walls composed of several layers (e.g. hollow block,plaster and ceramic), by directing confocal imaging of the medium arenot sufficient and lack accuracy since, inherently, in-wall imagingincludes imaging inhomogeneous objects, including hidden and unknownparameters and elements.

Prior methods and apparatus for in-wall or through wall imaging can beless than ideal in at least some respects. For example standard in-wallimaging solutions attempt to find reflectors (e.g. using a single pairof TX-RX antennas) rather than detect structure, and in most cases theperformance deteriorates when operating in composite media such ashollow-blocks.

In view of the above it would be desirable to provide improved imagingdevice that overcome at least some of the aforementioned problems withthe prior art.

REFERENCES

Bo Gong, Benjamin Schullcke, Sabine Krueger-Ziolek, Knut Moeller1, “Aclustering based dual model framework for EIT imaging: firstexperimental results,” Current Directions in Biomedical Engineering.Volume 1, Issue 1, Pages 278-282, ISSN (Online) 2364-5504, DOI:10.1515/cdbme-2015-0069, September 2015.

Berin A. Boughto. Dinaiz Thinagaran. Daniel Sarabia. Antony Bacic. UteRoessner, “Mass spectrometry imaging for plant biology: a review”.

Zhu Ming; Wu Sidong; Fu Kechang; Jin Weidong, “Radar signals clusteringbased on spectrum atom decomposition and kernel method,” in Measurement,Information and Control (ICMIC), 2013 International Conference on , vol.02, no., pp. 843-846, 16-18 Aug. 2013.

Xiang-Peng Zhu; Ming Jin; Wei-Qiang Qian; Shuai Liu; Yu-Mei Wei, “TheApplication of Unsupervised Clustering in Radar Signal PreselectionBased on DOA Parameters,” in Pervasive Computing Signal Processing andApplications (PCSPA), 2010 First International Conference on , vol.,no., pp. 956-959, 17-19 Sep. 2010.

Timothy C. Havens*a, K. C. Hoa, Justin Farrella, James M. Kellera,Mihail Popescua, Tuan T. Tonb, David C. Wongb, and Mehrdad Soumekhc,“Locally-Adaptive Detection Algorithm for Forward-LookingGround-Penetrating Radar”, Dept. of Electrical and Computer Engineering,University of Missouri, Columbia, Mo., USA 65211.

SUMMARY OF INVENTION

Prior to the summary of the invention being set forth, it may be helpfulto set forth definitions of certain terms that will be used hereinafter.

The term “discrimination method” as used herein is defined as a methodoperated over a set of data vectors, whose output is a short descriptionof each data vector in the set, by means of an index or a smaller vectorof numbers, with the purpose of discriminating differenttypes/categories of vectors in the data set from each other. Forexample, clustering (i.e. assigning an index to each vector), or PCA(Principal component analysis) are discrimination methods.

The term “type” as used herein is defined as a short descriptionassigned to data vector in the set.

According to a first aspect of the invention there is provided a devicefor imaging an object, the object comprises a plurality of parts, thedevice comprising: an antenna array comprising a plurality of antennas;a measurement unit configured to transmit signals to the antenna arrayand to receive signals from the antenna array; and a processor incommunication with said measurement unit, said processor is configuredto discriminate the received signals into types or categories, andgenerate an image of said object.

In an embodiment, said types are displayed by a displayer according tothe location where the received signals were measured on said object.

In an embodiment, said measurement unit is configured to perform areflective measurement by either a frequency sweep or a time pulse.

In an embodiment, said measurement unit is configured to extract phaseand amplitude of the received signals.

In an embodiment, the processor is configured to cluster said signalsinto at least two groups.

In an embodiment, the processor is configured to discriminate saidreceived signals according to a Principal Component Analysis (PCA).

In an embodiment, the processor is configured to discriminate saidreceived signals by comparing each signal of said received signals toother signals of said received signals by means of a distance metric,and allocate a high rank to signals which are significantly differentthan other signals as indicated by a high value of the said distancemetric.

In an embodiment, the device further comprising a location moduleconfigured to record the physical location of the antenna array andwherein the signals are measured by sliding the antenna array along athe surface of the object of interest, and wherein each signal is taggedwith the physical location at which it was measured.

In an embodiment, the processor is configured to cluster said taggedsignals into at least two groups.

In an embodiment, the processor is configured to discriminate saidsignals by using Principal Component Analysis (PCA).

In an embodiment, the processor is configured to discriminate saidsignals by comparing each signal to other signals, and allocate a highrank to signals which are significantly different than other signals.

In an embodiment, the processor is configured to display averageproperties of said signals.

In an embodiment, said average is an average power in a specific timerange or mean delay. In an embodiment, said measurement unit comprisesat least one transceiver, a signal generation and reception unit and anextraction unit.

According to a second aspect of the invention there is provided a methodof imaging the inner structure of an object, the method comprising:scanning the object by a device, said device comprises an antenna array,a measurement unit configured to transmit signals to the antenna arrayand to receive signals from the antenna array, and a processor foranalyzing said received signals; collecting the RF received signals intoa form of time signals tagged with physical location for limiting thetime signals to a region of interest; centering the received signals;arranging the received signal as a vector of data points; discriminatingthe data vectors into types; and displaying the types by a displayer.

In an embodiment, the centering of the received signals comprisesremoving the mean over all locations from the time signals.

In an embodiment, the method further comprising normalizing the receivedsignals by estimating an empirical standard deviation per time point andsignal, and dividing by said standard deviation.

In an embodiment, said standard deviation is estimated by averaging thesignal strength over a window of adjacent points in time.

In an embodiment, said discrimination step is performed according tomethods selected from the group consisting of: clustering, distance fromcluster center, PCA (Principal Component Analysis), anomaly error, RMS(compute an average delay) and intensity per time range.

In an embodiment, the method further comprising calibrating the receivedsignals by dividing the received signals by a known reference signal.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks, according toembodiments of the invention, could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein, areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed may best be understood by reference to thefollowing detailed description when read with the accompanying drawingsin which:

FIG. 1 shows a schematic diagram of an imaging device, according to anembodiment of the invention;

FIG. 2 shows a schematic diagram of an antenna array, according to anembodiment of the invention; and

FIG. 3 shows a flow chart of a method of in-wall object imaging,according to an embodiment of the invention.

DETAILED DESCRIPTION

The present invention relates to a system, device and method for imagingan object or substances and more specifically, but not exclusively, toMIMO (multiple-input and multiple-output) radar imaging such as in-wallimaging in the UWB frequency range.

According to some embodiments there are provided methods and device forimaging objects comprising unsupervised classifying and data analyzingof the object to detect and identify the structure of the object and insome cases further displaying the object's structure/underlyingstructure, e.g. the arrangement of and/or relations between the parts orelements of the object, according to the methods (for examplediscrimination methods) specified hereafter.

In some embodiments there are provided methods and devices to identifythe surface and internal parts of an object such as an object comprisingseveral parts (e.g. a hollow block containing cavities) by clusteringthe object's parts to a number of groups. In an embodiment, theclustering step comprises detecting or identifying that some parts ofthe object are of type “A” while others parts are of type “B” andproviding an image of the object, the image illustrating the identifiedparts of the object (e.g. clustering information, or structure data suchas type “A” and “B” parts). It is stressed that though the exact type ofthe object or the internal parts of the object are not identified inaccordance with embodiments the structure of the object is identifiedand may be imaged.

In some cases methods and device in accordance with embodiments may beused to detect the structure of a wall/medium (e.g. distinguish concretefrom hollow block).

In some cases, methods and device in accordance with embodiments may beused for through-wall imaging (mainly of nearby objects), scanning ofmaterials e.g. in production lines, etc.

The device is configured to or may scan an object or one or moreelements such as a wall or a surface. In operation, a plurality ofsignals are transmitted for example towards the wall/medium and thereflected signals are recorded (e.g. over a large bandwidth) by awaveform recording or frequency response recording unit or module,producing a measurement for a given location. This process is henceforthreferred to as “measurement”.

In some embodiments, a location unit or module such as an IMU (inertialmeasurement unit) or any other known device or method is used to recordthe physical location at which each measurement is taken. Then, themeasurements are discriminated in one of several known discriminationmethods e.g. clustering, and an image which describes the classificationresult is presented for example on a monitor device.

In some cases methods and devices in accordance with embodiments may beused to detect anomalies in a repetitive structure, while cancelling outthe deviations of the structure itself (e.g. in a hollow-block wall,detect that a group of blocks is different than the others, suggestingperhaps a leak, a pipe, or a different construction).

In some instances methods and devices in accordance with embodiments maybe used to detect objects behind a wall or surface.

In other instances the information gathered according to imaging methodsdescribed herein may serve as prior information for imaging and mediumestimation methods. For example, confocal imaging methods requireknowledge of the dielectric constant or propagation velocity. Forexample, a three step procedure may be used. In the first stage, thewall is scanned as described herein, and parts of the object withdifferent materials are identified (for example, part of the wall isdrywall and part is a concrete); at the second stage, the dielectricconstant of each part of the object is estimated from the signalsrecorded in each part (for example by methods as illustrated by thepresent invention applicant's PCT Application PCT/IL2016/050440, filedon Apr. 26, 2016, entitled “SYSTEM DEVISE AND METHOD FOR ESTIMATINGDIELECTRIC MEDIA PARAMETERS”). At the third stage, an imaging method isutilized according to the parameters estimated for each part of theobject separately.

Advantageously, the device and methods according to the invention do notrequire any prior knowledge on the object's modeling, for example, thetype of material, depth of arena, types of targets. Additionally, ityields an image of the underlying structure, which does not exist inprior in-wall imaging devices.

Reference is now made to FIG. 1 illustrating schematically a device 100in accordance with some embodiments. The device 100 comprises an antennaarray 104, a measurement unit 102 configured to transmit signals to theantenna array and to receive signals from the antenna array, one or moreprocessors 112 configured to discriminate the received signals intotypes or categories and a display or monitor device 114 for displayingthe structure of the object.

The antenna array comprises a plurality of antennas 105. The antennaarray may be for example a 4×3 antenna array. An example of such antennaarray is illustrated in FIG. 2 which shows antenna array 200 comprisingtwelve antennas. The measurement unit 102 may comprise at least onetransmitter and at least one receiver (e.g. transceiver 106), a signalgeneration and reception unit 108 and an extraction unit 110 such as anS-parameter extraction unit.

In some cases, each antenna 105 of the antenna array 104 may beconnected to a transceiver(s), such as transceiver 106 acting astransmitter(s) and/or a receiver(s). In some embodiments thetransceiver(s) 106 are operated in the UWB band (e.g. 3-10 Ghz) and mayperform a reflective measurement by either a frequency sweep or a timepulse. The signal generation and reception unit 108 generates thesignals sent to the transmitters and records the signals from thereceivers. The recorded signals may be further processed by theS-parameter extraction unit 110, which for example, extracts the phaseand amplitude of each received signal, and may perform variouscalibration procedures as known in the art. Such calibration proceduresmay include for example division by a reference signal, SOLT(Short-Open-Load) calibration, etc. The S-parameter extraction unit 110may further convert the result (i.e. phase and amplitude of eachreceived signal) to time-domain by for example means of inverse Fouriertransform for example in cases of frequency-domain measurements. Theresult of such measurements is an impulse response function between twoantenna ports of interest, which will be henceforth defined as “signal”result associated with the given antenna ports. This signal (impulseresponse) is affected by direct leakage between antennas but may also beaffected by the interfacing medium, and objects/material within it.

In some cases the antenna 104 or the measurement unit 102 may beconnected to or be in communication with a measurement unit or modulesuch measurement unit 116.

FIG. 3 is a flowchart illustrating a method 300 for imaging an object,such as an object which comprises a plurality of parts and displayingthe structure of the object. The image of the object may include forexample a display of the surface and inner portions of the object, suchas in-wall imaging or through wall imaging, in accordance withembodiments. The method starts at a sweep step 10 which includesscanning an object such as a wall or a surface by the device (e.g.device 100) by physically transferring the device along (for example inproximity to or on) the object. The sweep step 310 includes transmittingsignals from one or more of the device's antennas (step 312) andrecording the received signals (step 314). As an example, a signal maybe transmitted from each antenna and received by another antenna. Insuch case transmission coefficient (“S21” in microwaves' terminology) ismeasured.

In other instances, the receive antenna may be the same as the transmitantenna, so that the reflection coefficient (“S11” microwaves'terminology) is measured. The reflected signals can be recorded inaddition to, or instead of, measurements between different antennas(“through” measurements). These may be used to increase resolutionand/or in order to reduce the number of antennas needed (to 1 in theextreme case).

At step 320, the signals, for example each signal is tagged with itsrelative location on the surface of the object. For example, asillustrated in FIG. 2, a signal transmitted from antenna 1 and receivedby antenna 5 (denoted 1→5) of antenna array 200 is assumed to representa location lower by ΔY in the vertical direction compared to the signal(2→6) while the signal 5→9 is considered to be ΔX to the right of thesignal 1∛5, where ΔX, ΔY denote the antenna spacing in each axis of theantenna array in respect to a Cartesian axis (X-Y-Z) that is illustratedin FIG. 2. While the antenna array is moved along the surface, at step330 the location and orientation of the antenna array are recordedcontinuously by a location unit (such as inertial, optical or mechanicalunit which may be in communication or included in the imaging device),and the signals recorded at that point are tagged with their physicallocations along the surface (relative to the starting point).

In some cases, several signals (e.g. measurements) may be used duringthe processing together (e.g. concatenated or arranged into a vector oran array), to comprise a data set for a single location. For example insome cases for the physical location of antenna 5 (as shown in FIG. 2),the recorded signals (5→4, 1→9, 5→9, 5→1) will be aggregated in order tocharacterize a point on the object. In other cases, antennas atdifferent polarizations may be used in order to capture differentpolarimetric features of each point (e.g. VV, HH, HV reflections) of thescanned object. At each physical position of the array, several suchsets may be recorded and associated with different locations on theobject. For example, the set 5→5, 1→9, 5→9, 5→1 of antenna array 200 isassociated with the center of the bottom row in the array 200, and thereare 3 other such sets associated with each of the other rows (forexample, the respective set for the top row is 8→8, 4→12, 8→12, 8→4).

In some cases, calibration of the signals (step 316) may be required inorder to cancel out the effect of different trace/cable lengths andelectrical characteristics, e.g. by dividing the received signals by aknown reference signal. As the main purpose of calibration is toguarantee that the same physical reflection at different antenna pairswill be translated to the same recorded signal, this calibration may belearned and improved (trained) through the measurement process (withouthaving to use a reference signal or target). For example, a measurementof one or more symmetric targets (targets that yield the same reflectedsignals in all antennas, for example a mirror surface positionedparallel to the array at a distance of 10 cm from the array) may beperformed, and the reference signal may be determined so as to bring thesignals after division by the reference signal to be equal for all suchtargets. In the simplest case, the reference signal may be chosen as thereceived signal measured for one of the said symmetric targets.

At step 340, the measurement data is processed as follows:

-   -   1. Collecting the measurement data into a form of time signals        (step 342). For example the time signals are tagged with        physical location (each physical location may be associated with        one or more time signals, represented as a matrix of M time        signals by N points). For example, a device operating in the UWB        band (3-10 Ghz) and sampling at 50 Mhz spacing, and after        conversion to time domain by 2048-point IFFT, may produce        signals with N=2048 samples, covering the range 0 to 20 ns (1/50        Mhz) with sampling frequency 2048/20 ns=102 Ghz. Referring to        example given above for the 4×3 array 200, when 4 antenna pairs        are associated with each point, the matrix dimensions are M=4 by        N=2048, and 4 such matrices are recorded for each location (each        associated with a different row in the array and tagged with a        different location on the surface).    -   2. Limiting the time signals (step 344) to a region of interest        (to determine approximate depth range). For example, if the        depth of arena of interest is 20 cm and it is assumed the        dielectric constant does not exceed 5, the maximum two-way        propagation time is 0.3 ns. Continuing the above example, the        M=4 by N=2048 matrix will be trimmed to M=4 by N=32 matrix (N=32        samples roughly representing 0.3 ns).    -   3. Centering (step 346): Removing the mean over all locations        from all time signals.    -   4. Normalization (step 348): Normalization is performed by        estimating the empirical standard deviation (over all locations)        per time point and signal, and dividing by this standard        deviation.        -   a. In some embodiments the standard deviation may be            estimated by averaging the signal strength over a window of            adjacent points in time (providing a local normalization of            the signal).        -   b. In other embodiments, the standard deviation is estimated            from the multitude of data points associated with the same            pair and location. i.e. if L matrices of size M×N were            collected for L locations, the standard deviation estimate            is an M×N matrix where the element m,n is the standard            deviation of an L-element vector containing the (m,n)            elements of each of the L data matrices.        -   c. In other embodiments an estimate of standard deviation            combining fixed (a-priori) assumed standard deviation, and            the two aforementioned estimates is used.    -   5. Arranging the data (step 349) as a vector of data points per        location (composed of all M×N time points after processing). If        L locations were measured (including physical locations and        locations of different antenna-sets in the array), then a matrix        of size L times MN is formed, where each line (vector) in the        matrix is tagged with a physical location.    -   6. Discrimination (step 350): Processing the data matrix in one        or more discrimination methods described below, to obtain the        discrimination information.    -   7. Displaying the processing result (step 360), e.g. the image        of the object, by displaying an image comprising the        discrimination information, each displayed at a point matching        the physical location.

In some embodiments the processor is configured to provide or activateseveral modes of discrimination as follows:

-   -   Clustering: the data sets are clustered to a given number of        clusters (e.g. Nc=10 clusters), and the image may comprise a        color map where the color represents the cluster index.        Clustering may be done using one of several known methods such        as k-means clustering, Gaussian mixture models, and so forth.    -   Distance from cluster center: for each data vector, the distance        from the cluster-center associated with the cluster of that data        vector may be used as an indication of anomaly and/or success of        clustering for that vector. It may be displayed as an image or        used to determine the intensity of each point in the cluster-map        (i.e. color denotes cluster index, intensity denotes closeness        to cluster center). The following expression may be used for        normalized intensity:

${c_{intensity}\lbrack i\rbrack} = \frac{\sigma_{{Cluster}{\lbrack i\rbrack}}^{2}}{\sigma_{{Cluster}{\lbrack i\rbrack}}^{2} + {{{{data}\lbrack i\rbrack} - {{center}\left\{ {{cluster}\lbrack i\rbrack} \right\}}}}^{2}}$

where:

cluster[i] is the cluster index associated with data vector i, center{c}is the cluster center vector, σ_(c) ² is the cluster variance (the meanof ∥data[i]−center{cluster[i]}∥² over all points in the cluster). Otherdistance metrics may be used as well.

-   -   Principal component analysis (PCA)—PCA is applied to the data        matrix, and several (e.g. 3) strongest PCA coefficients are        translated to image colors. Other variants of PCA such as        non-linear/multi-linear PCA, or N-way PCA (higher order), well        known in the art, may be used.    -   Anomaly error: find the distance from closest signal amongst all        signals outside a “window” (i.e. not including the locations        directly surrounding the location associated with the signal).        The distance may be calculated using L2 norm, and/or by        extracting an empirical covariance matrix from the data and        using dist²(x₁,x₂)=(x₁−x₂)*Λ⁻¹(x₁−x₂) as the distance metric.        This distance metric is well known in the art as means for        highlighting deviations from the overall distribution (see ref        [5]). Alternatively, the distance from the mean signal may be        presented (i.e. replacing x₂ with the mean of all vectors in the        data matrix), or the covariance matrix Λ may be estimated for        each cluster separately.    -   RMS delay: compute an average (RMS) delay of each of the time        domain signals,

${{e.g.\mspace{14mu} {using}}\mspace{14mu} T_{rms}} = \frac{\int{{t \cdot {{x(t)}}^{2}}{dt}}}{\int{{{x(t)}}^{2}{dt}}}$

-   -   Signal intensity at various depth ranges. For example, the        signal intensity (at each point) may be measured for 3 depth        ranges for each location (since the propagation velocity is not        known, the depth ranges are actually time ranges), and encoded        to RGB color.

In some cases, following a rough discrimination process over thesurface, the user may select an area to focus on and re-run thediscrimination method on that area, in order to give finer details ofthe material. For example, after observing two distinct structures in awall, the user would like to focus on each part and determine itsinternal structure. In some cases, no additional signal acquisition isneeded, and the process is based on the signals recorded in the firstsweep.

In some cases, on-line clustering/analysis of the data may be performedin order to present to the user the type of material during the scanitself.

In some cases, the location information of the antenna array in eachgiven moment is further improved upon using the data collected asdescribed above. For example, if the locations provided are in-accurate,then after laying the discrimination data into a 2D image, there mightbe shifts between rows and/or columns of the image. These small shiftscan be estimated and corrected, e.g. using cross correlation betweenadjacent “rows” or “columns”. The shift estimation (e.g. crosscorrelation) may be applied to the image pixels, the discriminationdata, or directly to the signals (the M×N data matrices) associated witheach location. This can be used in order to improve the row and columnalignment in the case of location drifts.

In some cases, a static array may be used for obtaining the sameinformation from multiple antenna locations, without involving aphysical motion.

In some cases, the same concepts of obtaining discrimination informationmay be applied to the temporal rather than the spatial domain. Forexample, radar measurements of the same room are taken at differenttimes, and a discrimination method is used in order to separate outdifferent situations (e.g. detect if one or two cars are park in thegarage; detect an abnormal situation).

The description above relates to discrimination operated over the signalrepresented in time domain; however the same processing can be appliedto different representations of the data. For example, direct processingof signals in frequency domain (without time to frequency conversion, ifthey are measured in frequency), by observing the complex phasors, theamplitudes or the phases, with or without additional filtering.

In some embodiments of the invention, a discrimination process may beperformed on spatially processed signals rather than the directlyrecorded signals; for example, beamforming, which is well known in theart, where signals from different antennas are combined with complexweights or time delays, in order to maximize reception from a certainspatial direction. Alternatively, imaging methods such as delay-and-sum(DAS), a collection of signals from all antenna pairs are firstprocessed (by applying time delays in the case of DAS) to create avector comprising of a sample from each pair, and then summed togenerate the image for a certain voxel. The discrimination methods canbe applied to the vector of samples before summation.

In further embodiments, the processing unit or one or more processorsmay be a digital processing device including one or more hardwarecentral processing units (CPU) that carry out the device's functions. Instill further embodiments, the digital processing device furthercomprises an operating system configured to perform executableinstructions. In some embodiments, the digital processing device isoptionally connected a computer network. In further embodiments, thedigital processing device is optionally connected to the Internet suchthat it accesses the World Wide Web. In still further embodiments, thedigital processing device is optionally connected to a cloud computinginfrastructure. In other embodiments, the digital processing device isoptionally connected to an intranet. In other embodiments, the digitalprocessing device is optionally connected to a data storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers,handheld computers, Internet appliances, mobile smartphones, tabletcomputers, personal digital assistants, video game consoles, andvehicles. Those of skill in the art will recognize that many smartphonesare suitable for use in the system described herein. Those of skill inthe art will also recognize that select televisions with optionalcomputer network connectivity are suitable for use in the systemdescribed herein. Suitable tablet computers include those with booklet,slate, and convertible configurations, known to those of skill in theart.

In some embodiments, parameters of the discrimination method (forexample, depth range, number of clusters) are chosen by various criteriaon the resulting image, such as to highlight the properties that mightbe of more interest to the user. For example, selecting the depth rangeto operate upon (from a set of possible ranges), by minimizing a metric,such as Total Variation metric on the resulting image. The rationale isto search for the depth range in which the result appears to be an imageof some object, rather than random noise.

Although various features of the invention may be described in thecontext of a single embodiment, the features may also be providedseparately or in any suitable combination. Conversely, although theinvention may be described herein in the context of separate embodimentsfor clarity, the invention may also be implemented in a singleembodiment.

Reference in the specification to “some embodiments”, “an embodiment”,“one embodiment” or “other embodiments” means that a particular feature,structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the inventions.

It is to be understood that the phraseology and terminology employedherein is not to be construed as limiting and are for descriptivepurpose only. The principles and uses of the teachings of the presentinvention may be better understood with reference to the accompanyingdescription, figures and examples.

It is to be understood that the details set forth herein do not construea limitation to an application of the invention.

Furthermore, it is to be understood that the invention can be carriedout or practiced in various ways and that the invention can beimplemented in embodiments other than the ones outlined in thedescription above.

It is to be understood that the terms “including”, “comprising”,“consisting” and grammatical variants thereof do not preclude theaddition of one or more components, features, steps, or integers orgroups thereof and that the terms are to be construed as specifyingcomponents, features, steps or integers.

If the specification or claims refer to “an additional” element, thatdoes not preclude there being more than one of the additional element.

It is to be understood that where the claims or specification refer to“a” or “an” element, such reference is not be construed that there isonly one of that element.

It is to be understood that where the specification states that acomponent, feature, structure, or characteristic “may”, “might”, “can”or “could” be included, that particular component, feature, structure,or characteristic is not required to be included.

Where applicable, although state diagrams, flow diagrams or both may beused to describe embodiments, the invention is not limited to thosediagrams or to the corresponding descriptions. For example, flow neednot move through each illustrated box or state, or in exactly the sameorder as illustrated and described.

Methods of the present invention may be implemented by performing orcompleting manually, automatically, or a combination thereof, selectedsteps or tasks.

The descriptions, examples, methods and materials presented in theclaims and the specification are not to be construed as limiting butrather as illustrative only.

Meanings of technical and scientific terms used herein are to becommonly understood as by one of ordinary skill in the art to which theinvention belongs, unless otherwise defined.

The present invention may be implemented in the testing or practice withmethods and materials equivalent or similar to those described herein.

While the invention has been described with respect to a limited numberof embodiments, these should not be construed as limitations on thescope of the invention, but rather as exemplifications of some of thepreferred embodiments. Other possible variations, modifications, andapplications are also within the scope of the invention. Accordingly,the scope of the invention should not be limited by what has thus farbeen described, but by the appended claims and their legal equivalents.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

What is claimed is:
 1. A method of imaging the inner structure of anobject, the method comprising: scanning the object by a device, saiddevice comprising: an antenna array, a measurement unit configured totransmit signals to the antenna array and to receive signals from theantenna array, and a processor for analyzing said received signals;arranging the received signals as vectors of data points per location;processing the vectors of data points per location to obtaindiscrimination information, grouping the vectors of data points intoclusters; assigning a cluster index for each signal of said vectors ofdata points such that signals belonging to the same cluster have thesame cluster index; translating said cluster indices to colors accordingto a color map where each color is associated to a cluster index toobtain said discrimination information; and displaying an image of theobject, said image comprising said discrimination information, whereineach discrimination information relates to a point matching a physicallocation.
 2. The method of claim 1, further comprising: recording thephysical location of the antenna array, using a location module.
 3. Themethod of claim 2, further comprising: measuring the signals by slidingthe antenna array along a surface of the object; and tagging each of thereceived signals with the physical location at which said each of thereceived signals was measured.
 3. The method of claim 1, furthercomprising: calculating for each vector of said vectors of data points,a distance from the cluster center associated with the cluster of thatvector; and determining the intensity of each point in the said displayof the object image using said calculated distance.
 4. The method ofclaim 1, further comprising performing a reflective measurement byeither a frequency sweep or a time pulse using said measurement unit. 5.The method of claim 1, further comprising extracting the phase andamplitude of the received signals.
 6. The method of claim 3, furthercomprising averaging said tagged signals.
 7. The method of claim 6,wherein the result of said averaging is the signal average power in aspecific time range or in a specific mean delay.